Systems and methods of intelligent and directed dynamic application security testing

ABSTRACT

Disclosed are systems, methods and computer readable mediums for intelligent and directed dynamic application security testing. The systems, methods and computer-readable mediums can be configured to receive an attack location and an attack type for a web-application, transmit the attack location and attack type to a ID-DAST platform, receive from the ID-DAST platform a payload, attack the web-application using the payload, and receive results of the attack.

FIELD OF TECHNOLOGY

The present technology pertains to dynamic application security testing(DAST) and more particularly to intelligent and directed dynamicapplication security testing. The present technology also pertains to,for example, navigated or guided application security testing.

BACKGROUND

Traditional DAST technology is a process for web-application securityanalysis that utilizes crawling results to perform test attacks on agiven web-application (that is running), analyzing the web-application'sreactions to those attacks, and reporting detected web-applicationvulnerabilities that can potentially be exploited by actual attacksexecuted by hackers.

Traditional DAST is a process that gathers information and attacks theweb-application based on the gathered information. The source ofintelligence for traditional DAST technology is crawling. That is, DASTtechnology crawls a web-application, for example, page-by-page,form-by-form, and/or link-by-link. The results of the crawl are used todiscover potentially vulnerable locations which should be attacked(e.g., during testing) in order to validate whether the locations arevulnerable to the actual attacks by hackers. The results of the crawlcan also be used to determine the type of the test attacks and thepayload (e.g. format and parameters) for the test attacks.

The major challenge of crawling is its time-consuming nature and lack ofinsight into the web-application that is being tested. In order todiscover a potential attack location and type, DAST crawls theweb-application (e.g., page-by-page, form-by form, link-by-link) whichis an extremely time-consuming process. This time-consuming nature oftraditional DAST is exacerbated by the lack of insight into theapplication. To compensate for that lack of insight, DAST launches anexcessive number of time-consuming attacks with the expectation that atleast one or a few of them will find exploits. The time-consuming natureof this process and lack of application insight prohibits traditionalDAST from being effectively utilized while applications are activelybeing developed. This process therefore prohibits traditional DAST frombeing effectively employed during the application development process.

Static Application Security Testing (SAST) technology scans and analyzesthe source code, bytecode or binary code to find vulnerabilities insideof the code. SAST is also a time-consuming process because the entirecode is scanned and analyzed to determine potential vulnerabilities.Furthermore, the vulnerabilities reported by SAST require furtherresearch to determine if they are indeed exploitable at runtime.

Interactive Application Security Testing (IAST) technology utilizes anagent to provide security analysis on an application. IAST combinesfeatures of SAST and DAST technologies to determine vulnerabilities inan application. IAST technology has several challenges, including: IASTagent's instrumentation into the application runtime environment (e.g.,into Java Virtual Machine (JVM)) concerns application users/enterprises,as it can negatively impact application stability, including a collapseof the application; IAST is language-dependent (e.g., IAST requires aseparate agent for each programming language's analysis); and IAST isprocessor-consuming (e.g., IAST uses the same processor that runs theapplication), which can impact application behavior.

SUMMARY OF THE INVENTION

Disclosed are systems, methods and non-transitory computer-readablemediums for intelligent and directed dynamic application securitytesting. The systems, methods and non-transitory computer-readablemediums can be configured to receive an attack location and an attacktype for a web-application, transmit to a ID-DAST platform the attacklocation and attack type, receive from the ID-DAST platform a payload,attack the web-application using the payload, and receive results of theattack.

In some instances, the attack location and the attack type can bedetermined by automated analysis of a trace of APIs detected, forexample, by monitoring the execution of the application tested for thequality assurance (QA) purpose. In some instances, the attack locationand the attack type can be automatically determined based on an analysisof the code of the web-application as it is being written/modified bythe developer. In some instances, the attack location and the attacktype can be provided by at least one of: a developer, static applicationsecurity testing, and security analytics/statistics (e.g., Open WebApplication Security Project Top 10, etc.).

In some instances, the ID-DAST platform can be configured to exercisehistorical attack scenarios for web-applications. In some instances, thehistorical attack scenario includes at least a sequence of stepsrequired before submitting the attack payload. Historical attackscenarios can then be saved to a file, database, etc. and then re-usedto perform automated security testing.

The vulnerabilities, in some instances, can also be automaticallyverified (e.g., by a neural network, etc.) The systems, methods andnon-transitory computer-readable medium can also be configured totransmit the results of the attack to a neural network, and receiveverification the attack was successful. In some instances, the neuralnetwork uses historical request and response pairs for the verification.

BRIEF DESCRIPTION OF THE FIGURES

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1A illustrates an example environment of intelligent and directeddynamic application security testing in accordance with an embodiment;

FIG. 1B illustrates an example environment of intelligent and directeddynamic application security testing in accordance with an embodiment;

FIG. 2 illustrates a flow chart of an example method of intelligent anddirected dynamic application security testing in accordance with anembodiment;

FIG. 3 illustrates an example attack configuration file in accordancewith an embodiment;

FIG. 4A illustrates an example machine learning environment ofintelligent and directed dynamic application security testing inaccordance with an embodiment;

FIG. 4B illustrates an example machine learning environment ofintelligent and directed dynamic application security testing inaccordance with an embodiment; and

FIG. 5 illustrates an example system in which various embodiments can beimplemented.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationscan be used without parting from the spirit and scope of the disclosure.

Overview

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Techniques described and suggested herein include methods, systems andprocesses to perform intelligent and directed dynamic applicationsecurity testing (ID-DAST). Currently, DAST is a slow and cumbersomeprocess which includes two phases: a crawling phase and an attack phase.At the crawling phase, DAST crawls through the web application (pages,links, forms, etc.), which is a time-consuming and intensive process.The crawl results are provided to an attacker, which uses the crawlingresults to perform multiple tests and in some instances, tests onportions of the application that do not contain vulnerabilities (e.g.,not needed). In addition, DAST may test the application forvulnerabilities specific to various vendor/technology stacks (e.g.various databases), while the application uses just one particularvendor/technology stack (e.g. just one particular database) and requirestesting only for vulnerabilities related to that specificvendor/technology stack (e.g. database), thus making all other testsredundant and time-consuming.

ID-DAST, however, removes this time-consuming and cumbersome process byeliminating the crawling phase and instead relying on specific testinginformation provided, for example, automatically, by a trace of APIsdetected by monitoring the execution of the application that undergoesQA test. In some other instances, the testing information can beprovided, for example, automatically based on a changing applicationfeatures/functionality (e.g., changes to the code base, etc.). In otherinstances, the testing information can be provided by other analyses(e.g., SAST, etc.) or a developer. By eliminating the crawling phase,and instead relying on other sources of intelligence, ID-DAST candetermine specific attacks to perform in a condensed and efficientmanner, improving the dynamic application security testing process,while removing the unnecessary aspects of the process (i.e., reducingscope of tests performed based on provided intelligence (such asknowledge of executed API trace or source code)).

DESCRIPTION

The disclosed technology addresses the need in the art for improveddynamic application security testing. Disclosed are systems, methods,and computer-readable storage media for attacking and verifyingvulnerabilities using intelligent and directed dynamic applicationsecurity testing. A description of an example environment, asillustrated in FIG. 1A, is first disclosed herein. A description ofanother example environment, as illustrated in FIG. 1B, is nextdisclosed herein. A discussion of example processes for intelligent anddirected dynamic application security testing as illustrated in FIG. 2will then follow. A discussion of example attack configuration file asillustrated in FIG. 3 will then follow. A discussion of an examplemachine learning environment and corresponding processes as illustratedin FIG. 4A will then follow. A discussion of another example machinelearning environment and corresponding processes as illustrated in FIG.4B will then follow. The discussion then concludes with a description ofexample devices, as illustrated in FIG. 5. These variations shall bedescribed herein as the various embodiments are set forth. Thedisclosure now turns to FIG. 1A.

FIG. 1A shows an example intelligent and directed dynamic applicationsecurity testing (ID-DAST) environment 100. Environment 100 can includeone or more computing systems 102. Computing systems 102 can run one ormore integrated development environments (IDE) 104. IDE 104 can includea source code editor, build automation tools, compiler, versioncontroller, a debugger, plugin application 106 and attacker 108. In someinstances, the plugin application and attacker can be independent of theIDE (e.g., the plugin application could reside on a build server, whileattacker could reside on ID-DAST platform). Computing systems 102 cancommunicate with ID-DAST platform 110 and web application 112 vianetwork(s) 114. Network(s) 114 can be any type of computer network ordata link capable of transmitting and receiving data packets betweenComputing system 102 and ID-DAST platform 110 and web application 112.For example, network(s) 114 can be a local area network, wide areanetwork, wired network, wireless network, short range wireless network,the Internet, etc. In some example, computing system 102 can beconnected locally (or hardwired) to either or both of ID-DAST platform110 and web application 112. For example, web application 112 andID-DAST platform can be hosted on computer systems 102.

ID-DAST platform 110 can be one or more computing systems and databasesconfigured to store and organize results data from historical systemattacks. These historical system attacks (e.g., attack scenarios) can becaptured, for example, by using JSON configuration files. Theconfiguration files can be developed by a developer, can beautogenerated via IDE plugin, or via SAST scanning technologies.

ID-DAST platform 110 can receive from plugin 106 a proposed attacklocation (e.g., uniform resource locator) and an attack type (e.g. SQLInjection (SQLi), Cross-Site Scripting (CSS), sensitive data exposure,etc.). The ID-DAST platform can analyze the received data and determine,based on the historical attack data, what type attack payload would bebest suited for testing the proposed attack target, for example as shownin FIG. 3. The ID-DAST platform can then send a payload (e.g., asequence of steps to carry out an attack scenario selected out of thestored attack scenarios). In a not limiting example of a payload, anexample payload for a SQL injection, for example, on a first class ofvulnerabilities that are being testing can be: ′ or ‘a′=’a. The proposedattack location can be, typically, an API of the web application or itsgraphical user interface (GUI). As such, ID-DAST, through its plugin,can detect an application's APIs, which are then being tested by theattacker, thus fulfilling so called “API Testing”. Accordingly, ID DASTis capable of testing web application GUI as well as its APIs.

The attacker, upon receiving a payload from the ID DAST platform, canlaunch an attack against a web application. The attacker will collectdata about results of the attack and pass it to ID DAST Platform forreporting and, possibly, for verification.

In some instances, the ID-DAST platform can perform basic heuristics todetermine if a vulnerability was found (e.g., examining a test responseto determine if any error code or codes indicate a vulnerability ispresent). In some instances, the ID-DAST platform can pass collectedinformation (e.g., attack class, attack location, attack payload, attackrequest, attack response, etc.) to an existing machine learningimplementation for verification.

Web application 112 can be, in some examples, the unit-build or masterbuild that are in development. The master build includes the source codeof the individual unit-builds created and/or edited by one or moresoftware developers in, for example, an IDE. In some instances,potential vulnerabilities can be detected at the “Programming” phase ofthe Software LifeCycle (SLC), within the unit of the code that isprogrammed in IDE, or at the “Build” phase of the SLC, in a Buildserver, which Build integrates units of the code programmed by thedeveloper(s). ID-DAST can enable testing in “Development” and “Build”phases of the SLC. For example, testing in the “Build” phases can beperformed via attack scenarios as described above. Plugin application106 can work in combination with attacker 108 to monitor, analyze andtest source code written within IDE 104.

In one example, plugin application 106 can be installed in an integrateddevelopment environment (IDE). The plugin application can monitorchanges in the source code (e.g., via a representation of the sourcecode). For example, when a developer writes new source code or modifiesexisting source code in the IDE, the plugin application can monitor thechanges that are made. When the plugin application determines change(s)are made, the plugin application can analyze the changes to determinewhat type(s) of change(s) are made, for example, but not limited tochanges in: HTTP protocols (headers, parameters, HTTP content type),interaction with database, interaction with file system, URL,parameters, network traffic for attack, etc. Based upon the analysis ofthe changes by the plugin application, the plugin application cansuggest one or more attacks to test the changes. If the pluginapplication cannot determine the type(s) of change(s), a standard set ofattacks can be used (e.g., OWASP Top 10 attacks, etc.). The one or moreattacks and locations can then be provided to an attacker (e.g., 108)for carrying out the one or more attacks at the locations. In someexamples, the plugin application can operate on multiple programminglanguages, identify more potential vulnerabilities based on the detectedchanges, and extend the realm of the attacks by proposing one or morealternative attacks.

In another example, a static code analysis (e.g. SAST) could have beenperformed on the source code of the master build of a web applicationand would have located one or more vulnerabilities. Thesevulnerabilities can collect the same or similar data or information asdescribed in the preceding paragraph. The one or more vulnerabilitiesand associated data or information can be fed to the ID-DAST platform todetermine a payload to simulate one or more attacks on the master build.Since there are specific vulnerabilities that were identified, thosespecific vulnerabilities are specifically tested. In other instances,the static code analysis can describe where to attack, along withspecific vulnerabilities to test. The ID-DAST platform can validate theexploitability of static code analysis vulnerabilities in an automatedfashion.

In other examples, the most popular attacks can be performed on the unitand master builds. For example, the ten (10) most popular attacks canbe, for example, determined by the Open Web Application SecurityProject. In some examples, the vulnerabilities targeted can be thevulnerabilities most frequently found and or exploited based onhistorical data, annual security reports by security companies, or theannual data breach reports. In some examples, specific classes ofvulnerabilities can be targeted, for example: configuration file attack,form attack, database attack (e.g., SQL Injection, etc.), XML ExternalEntities (XXE), Directory Traversal, Cross-Site Scripting, InsecureDeserialization, HTTP Header Injection, OS Command Injection, Cross-SiteRequest Forgery, etc. In another example, attack scenarios used fortesting vulnerabilities in one or more individual IDEs by one or manydevelopers can be collected together and then used to test an entiremaster build.

FIG. 1B shows another example intelligent and directed dynamicapplication security testing (ID-DAST) environment 100 employing APItracing as a source of intelligence for its testing. Similar to FIG. 1A,the example shown in FIG. 1B illustrates environment 100 which includesone or more computing systems 102. Computing systems 102 can run one ormore web applications (or microservices) 112, which undergo a test, e.g.a QA test. Web application (or microservice) 112 can be integrated withID DAST plugin application 106, which dynamically captures API trace(for example, produced by analysis of the running application that isundergoing a QA test), and sends it to the ID DAST Platform 110.Computing systems 102 can communicate with ID-DAST platform 110 vianetwork(s) 114. As discussed above with respect to FIG. 1A, network(s)114 can be any type of computer network or data link capable oftransmitting and receiving data packets between computing system 102 andID-DAST platform 110. For example, network(s) 114 can be a local areanetwork, wide area network, wired network, wireless network, short rangewireless network, the Internet, etc. In some example, computing system102 can be connected locally (or hardwired) to ID-DAST platform 110.

In the embodiment illustrated in FIG. 1B, ID-DAST platform 110 storesand organize results data from historical system attacks. Thesehistorical system attacks (e.g., attack scenarios) can be captured, forexample, by using JSON configuration files. In this embodiment, theconfiguration files can be developed by a developer, can beautogenerated via IDE plugin, can be autogenerated via technologies thatmonitor network traffic to produce an API trace, or via SAST scanningtechnologies.

ID-DAST platform 110 illustrated in FIG. 1B can receive from ID-DASTPlugin 106 for example, a proposed attack location (e.g., uniformresource locator, as well as a method, parameters, and content-type of anetwork protocol) and an attack type (e.g. SQL Injection (SQLi),Cross-Site Scripting (CSS), sensitive data exposure, etc.). The ID-DASTplatform can analyze the received data and determine, based on thehistorical attack data, what type attack payload would be best suitedfor testing the proposed attack target, for example as shown in FIG. 3.The ID-DAST platform can then send a payload (e.g., a sequence of stepsto carry out an attack scenario selected out of the attack scenarios) toan Attacker 108. In a non-limiting example of a payload, an examplepayload for a SQL injection, for example, on a first class ofvulnerabilities that are being testing can be: ′ or ‘a′=’a. The proposedattack location can be, typically, an API of the web application or themicroservice. As such, ID-DAST, through its Plugin can automaticallydetect and then, through its Attacker test for security an applicationAPIs or its GUI, thus fulfilling so called “API Testing”. Accordingly,ID-DAST is capable of testing web application with its GUI as well asits APIs.

ID DAST does not require any preliminary, pre-application-runtimedeclaration of APIs by any party (e.g., a developer) or a technologyinvolved in application development, operation or security. APIdetection takes places via its plugin (e.g., Plugin 106). It happens atapplication-runtime automatically, thus making a preliminary APIdeclaration unnecessary. Moreover, a declaration made beforeapplication-runtime, can easily be inaccurate. For example, it can miss,conceal or misrepresent some API(s), or declare API(s) in a format thatviolates some established API-declaration standard(s). On the contrary,ID-DAST tests “real APIs”, i.e., APIs actually executed by the testedapplication, thus assuring API testing accuracy and coverage. Theattacker, in a similar manner described above, upon receiving a payloadfrom the ID-DAST platform, can launch an attack against a webapplication/microservice. The attacker will collect data about resultsof the attack and pass it to ID DAST Platform for verification.

Like the first embodiment, the ID-DAST platform can perform basicheuristics to determine if a vulnerability was found (e.g., examining atest response to determine if any error code or codes indicative of avulnerability is present). In some instances, the ID-DAST platform canpass collected information (e.g., attack class, attack location, attackpayload, attack request, attack response, etc.) to an existing machinelearning implementation for verification.

In the embodiment illustrated in FIG. 1B, web application (or amicroservice) 112 can be, in some instances, an individual unit-buildcreated by an individual developer. In some instances, web application(or a microservice) 112 can be a master build that includes unit-buildscreated by a team of multiple individual developers. There are differentways of detecting vulnerabilities in the web application (or microservice) 112. For example, vulnerabilities in the unit-build and/ormaster-build can be determined by analysis of a trace of APIs (e.g., atrace of APIs detected by monitoring the execution of the testedapplication that undergoes a QA test) and then attacking the applicationwith attack payloads. In some instances, potential vulnerabilities canbe detected at the “Programming” phase of the Software LifeCycle (SLC),within the unit of the code or unit of the build that is developed by anindividual developer, or at the “Build” phase of the SLC, in a Buildserver, which Build integrates units of the builds developed by theindividual developer(s). ID-DAST can enable testing at “Development” and“Build” phases of the SLC. For example, testing in the “Build” phasescan be performed via attack scenarios as described above. Pluginapplication 106 can work in combination with attacker 108 to monitor,analyze and test the web application (or micro service) 112.

In one example, a Plugin 106 is constructed as a listener of networktraffic (e.g., HTTP), which analyzes in-coming and outgoing networkrequests and responses. ID-DAST analyzes that traffic and detects traceof APIs. Upon APIs detection, in order to see if the APIs are vulnerableto attacks, ID-DAST either attacks those APIs in real time or recordsthose API for later API testing. Such API testing can be done forindividual unit-build (e.g. at Programming phases of software life cycle(SLC)) or for testing an entire application's master-build (at Buildphase of SLC).

Turning now to FIG. 2, which shows a flow chart of an example method forperforming an ID-DAST attack. Method 200 shown in FIG. 2 is provided byway of example, as there are a variety of ways to carry out the method.Additionally, while the example method is illustrated with a particularorder of blocks, those of ordinary skill in the art will appreciate thatFIG. 2 and the blocks illustrated therein can be executed in any orderthat accomplishes the technical advantages of the present disclosure andcan include fewer or more steps than illustrated.

Each block shown in FIG. 2 represents one or more processes, methods, orsubroutines, carried out in example method. The blocks illustrated inFIG. 2 can at least be implemented in the environments illustrated inFIGS. 1A and 1B. Additional steps or fewer blocks are possible tocomplete the example method. Each block shown in FIG. 2 can be carriedout by at least one or more of the systems illustrated in FIGS. 1A and1B.

Method 200 can begin at block 202. At block 202, a computing system(e.g., 102) can receive an attack location and an attack type. Theattack location and attack type can be identified and sent, for example,automatically, by a detected API trace; by a developer; by SAST; by aplugin into application's code, IDE or a runtime environment; byanalytics/risk statistics; by artificial intelligence analysis (e.g.,neural network), etc. All of these entities can have insight into where,what and why an attack should take place. In some examples, the insightcan be provided by various sources, for example, by analysis of an APItrace detected via monitoring the execution of the tested application;by code analysis via the plugin; by static application security testing;by developers, etc. For example, a developer could be working on a newmodule and can provide details of that module and a specific type ofattack to test if the new module has vulnerabilities before it iscommitted to the unit or master builds.

The attack location can be, for example, a uniform resource locator, aportion of source code, a module in a build, etc. The attack type can bebased on the underlying source code analysis. For example, the attacktype can be SQL Injection, XML External Entities (XXE), DirectoryTraversal, Cross-Site Scripting, Insecure Deserialization, HTTP HeaderInjection, OS Command Injection, Cross-Site Request Forgery, etc. Theattack types can also be, for example, a configuration file attack, formattack, database attack, etc.

At block 204, the computing system transmits the attack location andattack type to an ID-DAST platform (e.g., 110). The ID-DAST platform caninclude historical information on attacks that have been previouslyperformed. The historical information can include attack type, results,vulnerability, identifier, verification, remediation options,relationship to other vulnerabilities, etc. The historical informationcan be used to create attack payloads (e.g., a sequence of steps tocarry out a selected attack scenario), as shown in FIG. 3. Those attackpayloads can be stored in the attack payload database on ID-DASTplatform. Upon receiving the attack location and attack type, theID-DAST platform can analyze the received information and determine apayload. In a non-limiting SQL injection example, the plugin applicationcan notice changes to an application (e.g., source code) and suggestattack payloads for SQL Injection attacks. The plugin application candetect that the “Postgresql” database is used and provide thisinformation to the ID-DAST platform. In turn, the ID-DAST Platform canuse the suggested SQL Injection attack and information about the use ofPostgresql, and query one or more attack payload database for relevantattack payloads. Payloads can be associated with an attack class (i.e.SQL Injection) and tags (ex: database:postgresql) enabling querying ofthe one or more databases. In some instances, the payload can include asequence of steps to carry out a selected attack scenario (of thehistorical attack scenarios). In other instances, the payload caninclude a combination of historical attack scenarios.

At block 206, the computing system (e.g., 102) can receive the payload.Upon receiving the payload, the computing system can send or enableaccess of the payload to the attacker (e.g., 108). In some examples, theattacker can get the target web application/microservice into a desiredstate before submitting the attack (e.g., by submitting one or morecommands to the web application). In some examples, the attacker canperform a login step before submitting the actual attack, to enableaccess to the target functionality. At block 208, the attacker canattack the attack location using the payload. For example, the attackercan send a specific SQL Injections (e.g., payload as described above) totest a specific type of vulnerabilities at the attack location (e.g.identified by a uniform resource locator.

At block 210, the attacker (e.g., 108) can receive results of theattack. The results can include, for example, an SQL response. Uponreceiving the results, the attacker (or other components of thecomputing systems) can use heuristics based on the attack class andattack type to determine whether the attack was successful, for example,an attack may be deemed successful if the web application produces acertain error code or set of error codes in response to the attack. Inother examples, the results can be sent for verification to a machinelearning implementation as described below. In some examples, the attack(e.g., payload, location, attack type) and the results can betransmitted to a machine learning implementation, for example, a neuralnetwork, to verify the vulnerability as discussed below.

At block 212, the attacker or computing system (e.g., 102) can generatea report with results of the attack. For example, the report can includea line number and source file of where the vulnerabilities were located.In some examples, the report can include the line of code, where thevulnerability is detected; the type of the vulnerability; theexplanation of what the vulnerability means; and advice how to fix thevulnerability.

Turning now to FIGS. 4A and 4B, which show example ID-DAST environments100 with a neural network 116. The neural network 116 can be, but is notlimited to convolutional neural network (e.g., character-based, etc.),recurrent neural networks (e.g., long short term memory, etc.), etc. Theconvolutional neural networks (CNN) can classify the input textutilizing parameters such as length of the input array of numbers,vocabulary and convolutional filter configuration to enablevulnerability detection and verification. CNNs can be directly appliedto distributed or discrete embedding of words, without any knowledge onthe syntactic or semantic structures of a language. CNNs can also usecharacter-level features for language processing, for example, usingcharacter-level n-grams with linear classifiers and incorporatingcharacter-level features to CNNs. In particular, these approaches usewords as a basis, in which character-level features extracted at word orword n-gram level form a distributed representation. The long short termmemory networks (LSTMN) can classify, process and predict time seriesgiven time lags of unknown size and duration between important events(e.g., vulnerabilities, etc.). LSTMNs are popular in processing NaturalLanguage Processing (NLP) tasks because of its recurrent structure, thatis very suitable to process variable-length text, for example,distributed representations of words by first converting the tokenscomprising each text into vectors, which form a matrix that can includetwo dimensions: the time-step dimension and the feature vectordimension.

After attacker 108 receives results from either the web application (ormicro service) 112 (e.g., HTTP response), the attacker can transmit theresults to a neural network 416, for example, via network 114. Neuralnetwork 116 can receive the results (e.g., HTTP request/response) andcan verify whether or not the results of the attack are a vulnerabilityor a false positive. The neural network can then return to the attackerresults of the verification.

In some instances, neural network 116 can be multiple neural networks,each being specifically trained for different classes ofvulnerabilities. In these instances, when the results (e.g., HTTPrequest/response) are received a classification is first determined inorder to determine the appropriate neural network to perform theverification. In other examples, the attacker can send classificationdata with the results.

The neural network can be trained by ID-DAST platform 110. ID-DASTplatform 110 can include historical data sets, for example, attackresults (e.g., HTTP requests/responses) from one or more webapplications over a period of time (e.g., as described in FIGS. 1A and1B). The historical data sets can also include the vulnerability,identifier, verification, remediation options, relationship to othervulnerabilities, etc. The historical data sets can be continuouslyupdated with each attack. The historical data sets can be used to createattack files as shown in FIG. 3. The vulnerabilities detected (e.g.,historically) can be stored in the data store, along with associatedmetadata. Each vulnerability can have an identifier. The identifier canbe unique to the vulnerability. The corresponding metadata can also beused in identifying the potential vulnerability. When the vulnerabilityis being stored in the data store, the data store can determine if thevulnerability has been previously identified (via the identifier). Ifthe vulnerability has previously been identified, the data store candetermine if verification options and remediation options thatcorresponding to the potential vulnerability are stored at the datastore. When verifications and remediation options that correspond to thepotential vulnerability exist, during the store operation theverification options and remediation options can be associated with thenewly stored potential vulnerability and the associated metadata. Whenverifications and remediation options do not exist, a notification canbe set to the operator for further manual investigation. Subsequent, thevulnerability and associated data can be stored. In some examples, theverifications can determine the potential vulnerability is not avulnerability, for example a false positive. These vulnerabilities canbe classified, and each or a subset of these vulnerabilities can be usedto train one or more neural networks. The training, using the classifiedvulnerabilities, can create specialized neural networks that areintelligent in verifying potential vulnerabilities based on theavailable historical vulnerabilities and the associated data of thosevulnerabilities.

The neural network, once trained, can be used to analyze the results ofattacks. For example, when an attack is performed (as discussed in FIGS.1A and 1B) the attack (e.g., HTTP request) and the results of the attack(e.g., HTTP response) can be used as input into the trained neuralnetwork. The trained neural network can then determine if the attackresulted in a vulnerability, no vulnerability or a false positive. Ifthe attack resulted in a vulnerability, the vulnerability andcorresponding information can be provided in a report, as discussedabove. If the attack resulted in a vulnerability or a false positive theneural network can utilize that information for further training.

FIG. 5 shows an example of computing system 500 in which the componentsof the system are in communication with each other using connection 505.Connection 505 can be a physical connection via a bus, or a directconnection into processor 510, such as in a chipset architecture.Connection 505 can also be a virtual connection, networked connection,or logical connection.

In some embodiments computing system 500 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple datacenters, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 500 includes at least one processing unit (CPU orprocessor) 510 and connection 505 that couples various system componentsincluding system memory 515, such as read only memory (ROM) and randomaccess memory (RAM) to processor 510. Computing system 500 can include acache of high-speed memory connected directly with, in close proximityto, or integrated as part of processor 510.

Processor 510 can include any general purpose processor and a hardwareservice or software service, such as services 532, 534, and 536 storedin storage device 530, configured to control processor 510 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 510 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 500 includes an inputdevice 545, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 500 can also include output device 535, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 500.Computing system 500 can include communications interface 540, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Storage device 530 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs), read only memory (ROM), and/or somecombination of these devices.

The storage device 530 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 510, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable medium inconnection with the necessary hardware components, such as processor510, connection 505, output device 535, etc., to carry out the function.

Methods according to the aforementioned description can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can compriseinstructions and data which cause or otherwise configure a generalpurpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be binaries, intermediateformat instructions such as assembly language, firmware, or source code.Computer-readable media that may be used to store instructions,information used, and/or information created during methods according tothe aforementioned description include magnetic or optical disks, flashmemory, USB devices provided with non-volatile memory, networked storagedevices, and so on.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

The computer-readable storage devices, mediums, and memories can includea cable or wireless signal containing a bit stream and the like.However, when mentioned, non-transitory computer-readable storage mediaexpressly exclude media such as energy, carrier signals, electromagneticwaves, and signals per se.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Such form factors can include laptops, smart phones, smallform factor personal computers, personal digital assistants, rackmountdevices, standalone devices, and so on. Functionality described hereinalso can be embodied in peripherals or add-in cards. Such functionalitycan also be implemented on a circuit board among different chips ordifferent processes executing in a single device.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of information was used to explain aspects within thescope of the appended claims, no limitation of the claims should beimplied based on particular features or arrangements, as one of ordinaryskill would be able to derive a wide variety of implementations. Furtherand although some subject matter may have been described in languagespecific to structural features and/or method steps, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to these described features or acts. Suchfunctionality can be distributed differently or performed in componentsother than those identified herein. Rather, the described features andsteps are disclosed as possible components of systems and methods withinthe scope of the appended claims. Moreover, claim language reciting “atleast one of” a set indicates that one member of the set or multiplemembers of the set satisfy the claim.

The various embodiments further can be implemented in a wide variety ofoperating environments, which in some cases can include one or more usercomputers, computing devices or processing devices, which can be used tooperate any of a number of applications. User or client devices caninclude any of a number of general-purpose personal computers, such asdesktop, laptop or tablet computers running a standard operating system,as well as cellular, wireless and handheld devices running mobilesoftware and capable of supporting a number of networking and messagingprotocols. Such a system can also include a number of workstationsrunning any of a variety of commercially available operating systems andother known applications for purposes such as development and databasemanagement. These devices can also include other electronic devices,such as dummy terminals, thin-clients, gaming systems and other devicescapable of communicating via a network. These devices can also includevirtual devices such as virtual machines, hypervisors and other virtualdevices capable of communicating via a network.

Various embodiments of the present disclosure can utilize at least onenetwork that would be familiar to those skilled in the art forsupporting communications using any of a variety of commerciallyavailable protocols, such as Transmission Control Protocol/InternetProtocol (“TCP/IP”), User Datagram Protocol (“UDP”), protocols operatingin various layers of the Open System Interconnection (“OSI”) model, FileTransfer Protocol (“FTP”), Universal Plug and Play (“UpnP”), NetworkFile System (“NFS”), Common Internet File System (“CIFS”) and AppleTalk.The network can be, for example, a local area network, a wide-areanetwork, a virtual private network, the Internet, an intranet, anextranet, a public switched telephone network, an infrared network, awireless network, a satellite network, and any combination thereof.

In embodiments utilizing a web server, the web server can run any of avariety of servers or mid-tier applications, including HypertextTransfer Protocol (“HTTP”) servers, Hypertext Transfer Protocol Secure(“HTTPS”) servers, Transport Layer Security (“TLS”) servers, SPDY™servers, File Transfer Protocol (“FTP”) servers, Common GatewayInterface (“CGI”) servers, data servers, Java servers, Apache servers,Internet Information Services (“IIS”) servers, Zeus servers, Nginxservers, lighttpd servers, proxy servers (e.g., F5®, Squid, etc.),business application servers, and other servers (e.g., Incapsula™,CloudFlare®, DOSarrest, Akamai®, etc.). The server(s) can also becapable of executing programs or scripts in response to requests fromuser devices, such as by executing one or more web applications that canbe implemented as one or more scripts or programs written in anyprogramming language, such as Java®, C, C # or C++, or any scriptinglanguage, such as Ruby, PHP, Perl, Python®, JavaScript®, or TCL, as wellas combinations thereof. The server(s) can also include databaseservers, including without limitation those commercially available fromOracle®, Microsoft®, Sybase®, and IBM® as well as open-source serverssuch as MySQL, NoSQL, Hadoop, Postgres, SQLite, MongoDB, and any otherserver capable of storing, retrieving, and accessing structured orunstructured data. Database servers can include table-based servers,document-based servers, unstructured servers, relational servers,non-relational servers or combinations of these and/or other databaseservers.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationcan reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices can bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat can be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (“CPU” or “processor”), atleast one input device (e.g., a mouse, keyboard, controller, touchscreen or keypad) and at least one output device (e.g., a displaydevice, printer or speaker). Such a system can also include one or morestorage devices, such as disk drives, optical storage devices andsolid-state storage devices such as random access memory (“RAM”) orread-only memory (“ROM”), as well as removable media devices, memorycards, flash cards, etc.

Such devices can also include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor web browser. It should be appreciated that alternate embodiments canhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices can be employed.

Storage media and computer-readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as, but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer-readable instructions, data structures,program modules or other data, including RAM, ROM, Electrically ErasableProgrammable Read-Only Memory (“EEPROM”), flash memory or other memorytechnology, Compact Disc Read-Only Memory (“CD-ROM”), digital versatiledisk (DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices or any othermedium which can be used to store the desired information and which canbe accessed by the system device. Based on the disclosure and teachingsprovided herein, a person of ordinary skill in the art will appreciateother ways and/or methods to implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes can be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

Other variations are within the spirit of the present disclosure. Thus,while the disclosed techniques are susceptible to various modificationsand alternative constructions, certain illustrated embodiments thereofare shown in the drawings and have been described above in detail. Itshould be understood, however, that there is no intention to limit theinvention to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructionsand equivalents falling within the spirit and scope of the invention, asdefined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinthe range, unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. The use of the term “set” (e.g., “a set of items”) or “subset,”unless otherwise noted or contradicted by context, is to be construed asa nonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, the term “subset” of acorresponding set does not necessarily denote a proper subset of thecorresponding set, but the subset and the corresponding set can beequal.

Conjunctive language, such as phrases of the form “at least one of A, B,and C,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with the context as used in general to present that an item,term, etc., can be either A or B or C, or any nonempty subset of the setof A and B and C. For instance, in the illustrative example of a sethaving three members, the conjunctive phrases “at least one of A, B, andC” and “at least one of A, B and C” refer to any of the following sets:{A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of A, at least one of B and at least one of C eachto be present.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein (or variationsand/or combinations thereof) can be performed under the control of oneor more computer systems configured with executable instructions and canbe implemented as code (e.g., executable instructions, one or morecomputer programs or one or more applications) executing collectively onone or more processors, by hardware or combinations thereof. The codecan be stored on a computer-readable storage medium, for example, in theform of a computer program comprising a plurality of instructionsexecutable by one or more processors. The computer-readable storagemedium can be non-transitory (referred to herein as a “non-transitorycomputer-readable storage medium”) and/or can be tangible (referred toherein as a “tangible non-transitory computer-readable storage medium”).

The use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofthe invention and does not pose a limitation on the scope of theinvention unless otherwise claimed. No language in the specificationshould be construed as indicating any non-claimed element as essentialto the practice of the invention.

Embodiments of this disclosure are described herein, including the bestmode known to the inventors for carrying out the invention. Variationsof those embodiments can become apparent to those of ordinary skill inthe art upon reading the foregoing description. The inventors expectskilled artisans to employ such variations as appropriate and theinventors intend for embodiments of the present disclosure to bepracticed otherwise than as specifically described herein. Accordingly,the scope of the present disclosure includes all modifications andequivalents of the subject matter recited in the claims appended heretoas permitted by applicable law. Moreover, any combination of theabove-described elements in all possible variations thereof isencompassed by the scope of the present disclosure unless otherwiseindicated herein or otherwise clearly contradicted by context.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

What is claimed is:
 1. A system for intelligent directed dynamic application security testing, the system comprising: a processor; and a computer-readable medium storing instructions, which when executed by the processor causes the processor to: receive an attack location and an attack type for a web-application or a microservice; transmit, to a platform, the attack location and the attack type; receive, from the platform, a payload; attack the web-application or the microservice using the payload; and receive results of the attack.
 2. The system of claim 1, wherein the attack location and the attack type are provided by at least by one of: Application Programming Interfaces (APIs) detected via monitoring application traffic, a status of source/byte/binary code of the web application or the microservice, static application security testing, Open Web Application Security Project Top 10, and security analytics/statistics.
 3. The system of claim 2, wherein an Application Programming Interface detection and testing do not require a preliminary, pre-application-runtime declaration of APIs.
 4. The system of claim 1, wherein the platform includes historical attack scenarios for web-applications.
 5. The system of claim 4, wherein the payload includes at least a sequence of steps to carry out a selected attack scenario of the historical attack scenarios.
 6. The system of claim 1, comprising further instructions, which when executed by the processor causes the processor to: transmit the results of the attack to a neural network; and receive verification the attack was successful.
 7. The system of claim 1, wherein the neural network uses historical request and response pairs for the verification.
 8. A non-transitory computer-readable medium storing instructions, which when executed by at least one processor causes the at least one processor to: receive an attack location and an attack type for a web-application or a microservice; transmit, to a platform, the attack location and the attack type; receive, from the platform, a payload; attack the web-application or the microservice using the payload; and receive results of the attack.
 9. The non-transitory computer-readable medium of claim 8, wherein the attack location and the attack type are provided by at least by one of: Application Programming Interfaces (APIs) detected via monitoring application traffic, a status of source/byte/binary code of the web application or the microservice, static application security testing, Open Web Application Security Project Top 10, and security analytics/statistics.
 10. The non-transitory computer-readable medium of claim 9, wherein an Application Programming Interface detection and testing do not require a preliminary, pre-application-runtime declaration of APIs.
 11. The non-transitory computer-readable medium of claim 8, wherein the platform includes historical attack scenarios for web-applications.
 12. The non-transitory computer-readable medium of claim 11, wherein the payload includes at least a sequence of steps to carry out a selected attack scenario of the historical attack scenarios.
 13. The non-transitory computer-readable medium of claim 8, comprising further instructions, which when executed by the processor causes the processor to: transmit the results of the attack to a neural network; and receive verification the attack was successful.
 14. The non-transitory computer-readable medium of claim 8, wherein the neural network uses historical request and response pairs for the verification.
 15. A method comprising: receiving an attack location and an attack type for a web-application or a microservice; transmitting, to a platform, the attack location and the attack type; receiving, from the platform, a payload; attacking the web-application or the microservice using the payload; and receiving results of the attack.
 16. The method of claim 15, wherein the attack location and the attack type are provided by at least by one of: Application Programming Interfaces (APIs) detected via monitoring application traffic, a status of source/byte/binary code of the web application or the microservice, static application security testing, Open Web Application Security Project Top 10, and security analytics/statistics.
 17. The method of claim 16, wherein an Application Programming Interface detection and testing do not require a preliminary, pre-application-runtime declaration of APIs.
 18. The method of claim 15, wherein the platform includes historical attack scenarios for web-applications.
 19. The method of claim 15, wherein the payload includes at least a sequence of steps to carry out a selected attack scenario of the historical attack scenarios. 